Despite the wide variety of methods developed for synthetic image attribution, most of them can only attribute images generated by models or architectures included in the training set and do not work with unknown architectures, hindering their applicability in real-world scenarios. In this paper, we propose a verification framework that relies on a Siamese Network to address the problem of open-set attribution of synthetic images to the architecture that generated them. We consider two different settings. In the first setting, the system determines whether two images have been produced by the same generative architecture or not. In the second setting, the system verifies a claim about the architecture used to generate a synthetic image, utilizing one or multiple reference images generated by the claimed architecture. The main strength of the proposed system is its ability to operate in both closed and open-set scenarios so that the input images, either the query and reference images, can belong to the architectures considered during training or not. Experimental evaluations encompassing various generative architectures such as GANs, diffusion models, and transformers, focusing on synthetic face image generation, confirm the excellent performance of our method in both closed and open-set settings, as well as its strong generalization capabilities.

翻译：尽管目前已开发出多种合成图像归因方法，但大多数方法仅能识别训练集中包含的模型或架构所生成的图像，无法处理未知架构的归因问题，这严重限制了其在现实场景中的应用。本文提出一种基于孪生网络的验证框架，用于解决合成图像生成架构的开放集归因问题。我们考虑两种不同设置：第一种设置中，系统判定两幅图像是否由同一生成架构生成；第二种设置中，系统通过利用声称架构生成的一幅或多幅参考图像，验证关于合成图像生成架构的声明。本系统的主要优势在于无需依赖封闭集假设即可实现跨架构泛化能力，其输入图像（查询图像和参考图像）可来自训练期间考虑过的架构，也可来自未知架构。实验评估涵盖GAN、扩散模型和Transformer等多种生成架构，重点聚焦于人脸合成图像生成任务，结果证明该方法在封闭集和开放集场景中均具有卓越性能，同时展现出强大的泛化能力。